Name
Title | ||
|---|---|---|
Robust training of recurrent neural networks to handle missing data for disease progression modeling. |
Abstract | ||
|---|---|---|
Disease progression modeling (DPM) using longitudinal data is a challenging task in machine learning for healthcare that can provide clinicians with better tools for diagnosis and monitoring of disease. Existing DPM algorithms neglect temporal dependencies among measurements and make parametric assumptions about biomarker trajectories. In addition, they do not model multiple biomarkers jointly and need to align subjectsu0027 trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which hinders the application of standard RNNs and requires a pre-processing step such as imputation of the missing values. We, therefore, propose a generalized training rule for the most widely used RNN architecture, long short-term memory (LSTM) networks, that can handle missing values in both target and predictor variables. This algorithm is applied for modeling the progression of Alzheimeru0027s disease (AD) using magnetic resonance imaging (MRI) biomarkers. The results show that the proposed LSTM algorithm achieves a lower mean absolute error for prediction of measurements across all considered MRI biomarkers compared to using standard LSTM networks with data imputation or using a regression-based DPM method. Moreover, applying linear discriminant analysis to the biomarkersu0027 values predicted by the proposed algorithm results in a larger area under the receiver operating characteristic curve (AUC) for clinical diagnosis of AD compared to the same alternatives, and the AUC is comparable to state-of-the-art AUCs from a recent cross-sectional medical image classification challenge. This paper shows that built-in handling of missing values in LSTM network training paves the way for application of RNNs in disease progression modeling. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Receiver operating characteristic,Pattern recognition,Regression,Computer science,Recurrent neural network,Parametric statistics,Artificial intelligence,Missing data,Imputation (statistics),Linear discriminant analysis,Contextual image classification,Machine learning |
DocType | Volume | Citations |
Journal | abs/1808.05500 | 2 |
PageRank | References | Authors |
0.47 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mostafa Mehdipour Ghazi | 1 | 4 | 0.84 |
| Mads Nielsen | 2 | 1197 | 156.23 |
| Akshay Pai | 3 | 24 | 8.06 |
| Cardoso M. Jorge | 4 | 64 | 13.70 |
| Marc Modat | 5 | 898 | 72.33 |
| Sébastien Ourselin | 6 | 2499 | 237.61 |
| Lauge Sørensen | 7 | 215 | 17.78 |